{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "removed-detector",
   "metadata": {},
   "outputs": [],
   "source": [
    "import tushare as ts\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import warnings\n",
    "import sys\n",
    "import datetime\n",
    "import talib\n",
    "import numpy as np\n",
    "import statsmodels.api as sm\n",
    "\n",
    "\n",
    "sys.path.append(\"..\")\n",
    "\n",
    "from imp import reload\n",
    "\n",
    "\n",
    "from common.config_helper import config_helper\n",
    "from common.analysis_helper import analysis_helper\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "constant-commercial",
   "metadata": {},
   "outputs": [],
   "source": [
    "config = config_helper()\n",
    "start_date = \"20200101\"\n",
    "end_date = \"20201231\"\n",
    "pro = ts.pro_api(config.tushare_token)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "second-pontiac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>trade_date</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>600685.SH</td>\n",
       "      <td>20201231</td>\n",
       "      <td>24.00</td>\n",
       "      <td>26.58</td>\n",
       "      <td>23.91</td>\n",
       "      <td>26.58</td>\n",
       "      <td>24.16</td>\n",
       "      <td>2.42</td>\n",
       "      <td>10.0166</td>\n",
       "      <td>626615.43</td>\n",
       "      <td>1633441.950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>600685.SH</td>\n",
       "      <td>20201230</td>\n",
       "      <td>23.90</td>\n",
       "      <td>24.44</td>\n",
       "      <td>23.56</td>\n",
       "      <td>24.16</td>\n",
       "      <td>24.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>112889.17</td>\n",
       "      <td>270370.061</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>600685.SH</td>\n",
       "      <td>20201229</td>\n",
       "      <td>24.38</td>\n",
       "      <td>25.31</td>\n",
       "      <td>24.10</td>\n",
       "      <td>24.16</td>\n",
       "      <td>24.36</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.8210</td>\n",
       "      <td>105203.22</td>\n",
       "      <td>260162.007</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>600685.SH</td>\n",
       "      <td>20201228</td>\n",
       "      <td>24.99</td>\n",
       "      <td>25.05</td>\n",
       "      <td>24.20</td>\n",
       "      <td>24.36</td>\n",
       "      <td>25.25</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-3.5248</td>\n",
       "      <td>132036.21</td>\n",
       "      <td>323597.170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>600685.SH</td>\n",
       "      <td>20201225</td>\n",
       "      <td>25.50</td>\n",
       "      <td>25.50</td>\n",
       "      <td>24.63</td>\n",
       "      <td>25.25</td>\n",
       "      <td>25.82</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-2.2076</td>\n",
       "      <td>168412.72</td>\n",
       "      <td>421060.629</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ts_code trade_date   open   high    low  close  pre_close  change  \\\n",
       "0  600685.SH   20201231  24.00  26.58  23.91  26.58      24.16    2.42   \n",
       "1  600685.SH   20201230  23.90  24.44  23.56  24.16      24.16    0.00   \n",
       "2  600685.SH   20201229  24.38  25.31  24.10  24.16      24.36   -0.20   \n",
       "3  600685.SH   20201228  24.99  25.05  24.20  24.36      25.25   -0.89   \n",
       "4  600685.SH   20201225  25.50  25.50  24.63  25.25      25.82   -0.57   \n",
       "\n",
       "   pct_chg        vol       amount  \n",
       "0  10.0166  626615.43  1633441.950  \n",
       "1   0.0000  112889.17   270370.061  \n",
       "2  -0.8210  105203.22   260162.007  \n",
       "3  -3.5248  132036.21   323597.170  \n",
       "4  -2.2076  168412.72   421060.629  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_zcfw = pro.daily(ts_code=\"600685.SH\", start_date = start_date, end_date = end_date)\n",
    "df_zcfw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "numerous-danger",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ts_code</th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>pre_close</th>\n",
       "      <th>change</th>\n",
       "      <th>pct_chg</th>\n",
       "      <th>vol</th>\n",
       "      <th>amount</th>\n",
       "      <th>rtn</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>trade_date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2020-12-31</th>\n",
       "      <td>600685.SH</td>\n",
       "      <td>24.00</td>\n",
       "      <td>26.58</td>\n",
       "      <td>23.91</td>\n",
       "      <td>26.58</td>\n",
       "      <td>24.16</td>\n",
       "      <td>2.42</td>\n",
       "      <td>10.0166</td>\n",
       "      <td>626615.43</td>\n",
       "      <td>1633441.950</td>\n",
       "      <td>0.095461</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-30</th>\n",
       "      <td>600685.SH</td>\n",
       "      <td>23.90</td>\n",
       "      <td>24.44</td>\n",
       "      <td>23.56</td>\n",
       "      <td>24.16</td>\n",
       "      <td>24.16</td>\n",
       "      <td>0.00</td>\n",
       "      <td>0.0000</td>\n",
       "      <td>112889.17</td>\n",
       "      <td>270370.061</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-29</th>\n",
       "      <td>600685.SH</td>\n",
       "      <td>24.38</td>\n",
       "      <td>25.31</td>\n",
       "      <td>24.10</td>\n",
       "      <td>24.16</td>\n",
       "      <td>24.36</td>\n",
       "      <td>-0.20</td>\n",
       "      <td>-0.8210</td>\n",
       "      <td>105203.22</td>\n",
       "      <td>260162.007</td>\n",
       "      <td>-0.008244</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-28</th>\n",
       "      <td>600685.SH</td>\n",
       "      <td>24.99</td>\n",
       "      <td>25.05</td>\n",
       "      <td>24.20</td>\n",
       "      <td>24.36</td>\n",
       "      <td>25.25</td>\n",
       "      <td>-0.89</td>\n",
       "      <td>-3.5248</td>\n",
       "      <td>132036.21</td>\n",
       "      <td>323597.170</td>\n",
       "      <td>-0.035884</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2020-12-25</th>\n",
       "      <td>600685.SH</td>\n",
       "      <td>25.50</td>\n",
       "      <td>25.50</td>\n",
       "      <td>24.63</td>\n",
       "      <td>25.25</td>\n",
       "      <td>25.82</td>\n",
       "      <td>-0.57</td>\n",
       "      <td>-2.2076</td>\n",
       "      <td>168412.72</td>\n",
       "      <td>421060.629</td>\n",
       "      <td>-0.022323</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              ts_code   open   high    low  close  pre_close  change  pct_chg  \\\n",
       "trade_date                                                                      \n",
       "2020-12-31  600685.SH  24.00  26.58  23.91  26.58      24.16    2.42  10.0166   \n",
       "2020-12-30  600685.SH  23.90  24.44  23.56  24.16      24.16    0.00   0.0000   \n",
       "2020-12-29  600685.SH  24.38  25.31  24.10  24.16      24.36   -0.20  -0.8210   \n",
       "2020-12-28  600685.SH  24.99  25.05  24.20  24.36      25.25   -0.89  -3.5248   \n",
       "2020-12-25  600685.SH  25.50  25.50  24.63  25.25      25.82   -0.57  -2.2076   \n",
       "\n",
       "                  vol       amount       rtn  \n",
       "trade_date                                    \n",
       "2020-12-31  626615.43  1633441.950  0.095461  \n",
       "2020-12-30  112889.17   270370.061  0.000000  \n",
       "2020-12-29  105203.22   260162.007 -0.008244  \n",
       "2020-12-28  132036.21   323597.170 -0.035884  \n",
       "2020-12-25  168412.72   421060.629 -0.022323  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a_helper = analysis_helper(df_zcfw)\n",
    "a_helper.init_df()\n",
    "a_helper.df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "unique-huntington",
   "metadata": {},
   "outputs": [],
   "source": [
    "# rtn = a_helper.df.rtn\n",
    "# rtn = np.array(rtn)\n",
    "rtn = a_helper.df.rtn.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "expanded-destruction",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f15b9421588>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "model = sm.tsa.AR(rtn)# sm.tsa.ar_model.AR(rtn)\n",
    "\n",
    "\n",
    "fit_ar = model.fit()\n",
    "\n",
    "plt.figure(figsize=(10,4))\n",
    "plt.plot(rtn, 'b', label='return')\n",
    "plt.plot(fit_ar.fittedvalues, 'r', label='ar fit')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "interior-lindsay",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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mh2RmlkpHBtM4DthS8nkrcOYg418K/LjSF5IuAy4DmDdvXgahZaOvP/jAjQ+xeks3+3v6mNDZzpK5U7n50jNpb1OzwzMzq8mINtpK+gNgKfAPlb6PiBsiYmlELJ0xY8ZIhjaoFet3snpLN/t6+ghgX08fq7d0s2L9zmaHZmZWsywS/jZgbsnnOcmwASSdA1wNXBARBzKY74hZs30P+3v6Bgzb39PH2u17mhSRmVl6WST8h4GFkk6Q1AlcBCwvHUHSG4D/QyHZj7pi8SnHTmFCZ/uAYRM621l87JQmRWRmll7dCT8ieoHLgbuBdcBtEbFG0rWSLkhG+wdgMvB9SaslLa8yuUxl1dC67OSZLJk7FfX1QPQzManDX3byzIwjNjNrnCwabYmIO4E7y4ZdU/L3OVnMJ1VMKLOG1vY2cfOlZ3LWuy+lZ9JMPv/pT7Ds5JlusDWzUWXM3mm7f+oJmTa0treJid2bmLrtQc5eNMvJ3sxGnTGb8HsmzXJDq5lZiTGb8Dv37nBDq5lZiTGb8Cd0P+WG1hHkO5HNWl8mjbatSIQbWkeI70Q2Gx3GbAkf3NA6UnwnstnoMGZL+EV79h8E4IGNu1tqWmPJXU88V7GB/K4nnmNi55jfxcwyd9aCaQ2Zro/GjPT3B6u3dPP07r3MnzaJJXOn0paTK4r50ybR2dHGgd7+Q8M6O9qYP21SE6Mys3K5TvhZJen+/uBzP15H185X6Ontp7OjjZNmTuaq8xblIukvmTuVk2ZOZs0zz0N7B+PHdXDSzMksmTu12aE11Gg4yY+GGG3k5DbhZ5mkV2/ppmvnK4dKuAd6++na+Qqrt3Rz+vFHNyL8ltLWJq46bxF/8rFP0jd5Fpd/+LIxn1hGw0l+NMRoI2tMN9oOpjRJBwOTdFpP795LT0l1BkBPbz9P796bUbStr61NdO7uYsLm+zn9+KPHfELJcv9Jq78/WLX5RX64aiurNr9If5UusM2MsZlqXT95lNsS/mBJOm2p3HXY+ZPl/pNGmlJ7s2JsJl/VDC63Jfxiki413CRdrMOmt3CT1/hkJxvrddhZaZUSWZo4stx/0sSRptTeyBhbVV6vamqV2xJ+lg2NeazDzkqrlMjSxtGohuqh4khTas9jY3oer2rSyG0Jv5ikJ6/9EROe+jc++raFXPGO17F6S/ewSpqD1WG3Sgm2FbVKiSxtHJX2nyxOUkPFkabU3qgYW03p8dXXH7m7qkkjtyV8+FWSZncXS+Z+qiElzVYpwbaqVimRDSeO0v3n9OP/fETiSFtqb0SMraT8+BrXLsa1t3Ggtyc3VzVp5LaEX65RJc1WKcG2qlapZx7pOKpd9Q0VR15K7bUqP756+oKDff0cseUhr58KcpXwB6taaVTXSnfZHFyrNHiPZBzFUul1927g9ke2ct29G/jcj9fR3x81xZG3LrCDqXR8HewLFP1ePxXkpkqnWtVKIEQ0rGtls7tstvqdlq3S4D2ScQx1o14rrI/Rotrx1f7KjiZG1bpyk/CrHWTjpi2gc3dXw3o0NLqnRHlCf/1xR/H4tpd4evde5h0zkbueeI6uXa3dftAq9cwjFcdQ9fSNjKPVCwBpVTu+tu/eeGicsbbM9chNwq92kLVNngW7uxpWwmtkybFag1Vvf9DT2097m+iLIJKaq7w98gFa82Bv1lVf1h0Isly3w51WtePrT78Th6abZplbcX/JUm4Sfi2Xfo0qWTVquuVXLT19QU/frx5T3Fuh+2c9PWBG28FQ6WBfMGMS5506m2de2DfkMjRqeZvVPz7LZz7Vu26HmlaaE9Fgx1eaZW70CbH06rtZx09uEn4tl36jTaWrlqEMtyQ5GruXVjrY1z37Mht2vkJvXwy6DI1c3kql0tcfd9SA5FBsW8pSll1g067bwU6ejXz4YJplrhTHhh0v84NVW2lvU10nsfKr72YdP7lJ+ENd+o1Gla5aygmIvoPQ1l5XSbLSwbD+uZf56ooufn3B9ExLp1mVrCsd7EGhFwcMnlga/QTUoe4B6TvtIo587Na651Mqy6qkNOt2ydypmd09nFaaZa4YR1/wL49tp79/8AJCuaGuvptVvZpJt0xJ50paL6lL0hUVvh8v6XvJ9w9Jmp/FfNMaa93ZyrvwdbaLSZ3tA7r0LZp9JJPW1N9nu9LB0Nsf3L9x96FuhUF27RKVuiymValPe7lqXWTTdqet527qSvdq9E45joPTFtQ8jVpk2fW0lnV7oLef/9j4PF9d0cWTO17O5O7htNIsc7Vl6uuP1PfQ1HL13Yzu2XUnfEntwPXAecBi4GJJi8tGuxR4MSJOAr4I/H2987XDb8L52Nmv5Wvvf+OAm3KuPn8x43dvqPskN9gBXjwQskhQlZJf8bI6bTItP9g72oTKFr9aYkmThALVdZKqmBzaO+ibPKum39cqy5u2alm3An7x9Avcv3H3oZJ/UWmyq5SUF8yYRH9E3Y8jSbPM5XFUegd2rUm6lhNiM24wzKKEfwbQFRGbIqIHuBW4sGycC4FvJX/fDpwtle8eNhzlVy0dHW3DvooZrJRafjCU6+ntzyRBDXZZnTaZHn5CXMji10wZNLEUr1LSlAwPTltQ193UFZNDX29D+pLXc5Vbun+s3tLNFe94XdV129EmEIcl+qJx7aKvPypO6/K3noQQX7mvq+6rvDTLXL6/XHjasYwf5pVHLVffzbjBUBH11WFLeg9wbkT8UfL5A8CZEXF5yThPJONsTT5vTMZ5vtp0jzl+Ubz9qpuGFdPqx1YDsOS0JTyy6lEAFi4+FYANa5+o+XNE8MqBPl492McR49p5dtN/oirfTR7fTte6NQOmVap8PllKs0zVRATPvLCf/Qf7iAAJJoxrZ94xEyiemyOCJ7s2EeMmwvjJA5oUJdDLO2k7uH9Yy1iM8TUnvo5t3fsZbLeU4LipEzjyiNqaoMq36ZNdm6C9k9mzX8OL+w4OWObo2U/7Kzt5bdm4xx47m8nj2ykvp2xY+wT9RxxF/4TDD9wZkzuZfuT4IeM6adEpPPPCfvYdOAgItWlAHEMtU5rlH+q3lfZrSVX3jwM7Ng04Jorra/LR09nzam/5xAv/S7Sp8DE4fFqV9oGhtnkWx0D5tCptl/JjYjDl+8+kzjY2bHxq0P2paMoR44acfjW3ffjXH4mIpZW+a6mEL+ky4DKAybMXvPH8v7y5rtgA9rx6cFi/K+7glTY2UNOOMNhOlnYHLf1c78mj0rSqHWSVkngtJ4fhLGOlaVfTtr+btldfqrg+al0/L7/aW3diGWw65euulkR74GAf40sS7WDzrRZX2v2jlhPP7JRJuNI6AZhyRAfj2tt4YW/PYQWG4rR2vXyA51/pGfjDCNpefYmTT5xb0/qoZXmHs11KC31pp5UmjkYl/Cx66WwD5pZ8npMMqzTOVkkdwFHA7vIJRcQNwA0AS5cuje/9yVl1B/fAxsNmU5NVm1/kuns3gNqS2AqNN+98/XEAVb8rbXH/yHevBOCaTy0fMO3+/uBP7r6Rvsmz+N1f++1Cb6Gycct/W/q52nRrUT7vr3/3KkRw1jlf5fZHtg4YNwLGvbKD9n0vHIqzeDlcS0+awZah0jqoNO2+/uBff/nsgF4W4zvaGLfhJ3Tu7qq4PmpdPz9ctfWwZSbgrBOn8e7T51T8TaVpV+vCuf3ev0VE6rhqnW/p/Ivr8vxT387X7r6J/skzD1uvQ037ne/71mH7NWpnwpaHOOucN6daV4N1a/3R6m2DTqt47A3ofdZ3kIkbfsI1H/3CkOtjKMPdXwA+8vt/cWjcWo7j4cZx1oJpaRZpgNs+XP27LBL+w8BCSSdQSOwXAb9fNs5y4BLgAeA9wL1R76VFgw3VS2O43ciKB8Iri98F7R1cd++GAc/0aaRK8y52/6vUfU1Az4zXQVv7oTiLDV5tbeL0448e9g1cldZBpWn39wfrd7x8eDLN4P6JrLopFut+y0+AI9Hlt3xdfuGnTxK/9nsAh63XoQzWeJx2XVVbJ21Jf/bBplWs/x7QTfWFbYxroXtmmnkc16PuhB8RvZIuB+4G2oGbImKNpGuBlRGxHLgRuFlSF/AChZNCSxtqpxxuoij2QqGjEzj8mT6NVGneJN3/yg+y4mMZaB83IM5a+w339wc9006ib/IsVm1+cUDjVLV1UGnajUymlRJLPW89G+4JsB7l6zLgUAk97TareF9H0ng8nHVVbZ0MNa1K2/zrV/5NSyXSSvvwhh0v0zb/LSj6D+3zrdb1O5MbryLiTuDOsmHXlPz9KvBfspjXSBlqpxxuohjqmT6NNFgJrvwg2969n/8oqw7L6iom7Y02gyXTwU4sQxmsFDpaDNXfO80NTIOVrLNcV7VMq3ybt1Kyh+q9yTj+LFBb6qurkZKbO23TGmqnHO7O38zHuVYswfX30Tdx2qFkWTzIVm1+kZWbX2zIVUxWVSnF/u/1XFY3q2SelaHutk6zXocqWWe5ruqtEiw/yY90Uq263tsKKbVVH1SYqxegpFXcKd99+pzD+u8O9t1giqWo8R1tCA71xx2J+snSeUOhjp62dnpmLT6sr3O1OId7FXOgt5+emYvYf/yb6Y/gpBnDm3apYv93OjpBbZneADZaVNqmKnSBH9Z6Ld2vl8ydysFpC9h//Jtb5l3MpVeP+0/4jbr76A9X+fFRz01aI8kl/BHWzAa+0nn/x8bn+cXTL3Cwr3J9bz2X8EM1AH/lvi4WzJjE5W89KfWTFUv1TZ5V8cQSMxc1tfQ3ksq307xjJgLUtV5h6Ib1ZknT/lOL4V4tlK/3Sr3JWvHl6U74DTLYjpT2crZ8WvX0BCjO++nde+mtcrt7Ma7hXnbX0gC8cdde2qSq3R9r0f7KjlQ9i8aqSttp6fxj6ppm1ok1K1k+aC3tSa3SMT1Ub7Jar67Kp33GCcdUvGqolxN+A2RZOhqsK2U9GvkSjiwbgAczbvdGjs+wZ1EeVSuYNPIJlvXIcr9Nc1KrpSvxcK+IK037/o3Pc/OlZ2ae9F2H3wADdqSSuuVqz1YpHnSV6korTav3qDnsXfTOuupV66mjr0VpXfCvL5jekKchiuCq8xbx0bct5D1vnMOZJxxD+YVPK9ajpjXY/lHvdKvVhzfyCZb1yHK/TfNE1FqO6eG261Wa9uot3axYvzP1Mg3FJfwGSFM6Gk4XRto66Jl1Sl1XDiPZJTHL/u7lSqsz6ulZ1KoaWZc+WAm3kdssrdKrkOKD1rJ4c1Tdz8rP6Iqn0rT39/Sxdvsezl6U7RNTnfAbIM2ONJwujMXn0NZbZTFSXRJH6uTS6CSVpoGvnvsDSjWyLn2oJNYK9ygMdsKrd/nT7C+NrAKtNO0Jne0sPnZK3dMu54TfAGl2pKFuxCqdVqW+1q1Qr1qLkTi5NPLEkqakXWncBTMmcWDaQvonz0zVG6SZb4NqhXsUGtkrJ83VQiMLE5WmvWTuVJadPLPuaZdzwm+ANIlnqBuxKneljAHjjuYqi6w1KkmlSTyVxl337MvEKe9K3XuokSXLVqq2qWakeuUMNa1GFiYqTfvDyxa4l85oUmviqXTQLZgxic3oUCNdsfvXkrlT6a7wBMJWOkDHqjSJp9r7XofTe6jR7R+tUG0zmGb1yqmkkVc85dNuRLIHJ/ymq3TjzF1PPMfexRdWrDpo9QN0rEqTeGp5uXytpdRGb/NWqLYZTJYnvFbtajqSnPBbQHlPk65d1UshrX6AjlVpEk+1G89KHwie9hk3ed3mWZ7wGlk9Nlo44bcYl0JaU5rEU+2qrWuXq+KGI6sT3mhos2g0J/wW41JI60qTeMrHPX3e0a6KazJXiTrhtxyXQsamPFfLtJJW2Q7NesTzmE/49bwbslmWL3gLK9bvZO32PSw+dgrLTp7ZsFb70WrKhEKPl9G4fS3f+vqDD9z4EHtPeRfR1sH1Kwr32zTi2TnlxnzCH43a28TZi2Zlflu1mTXfivU7Wb2lm2gvdMzY19N36Nk5jT7m/fA0M7MRtGb7Hvb39A0YVnx2TqM54ZuZjaBTjp3ChM72AcMa9eycck74ZmYjaNnJM1kydyoTO9sRMLGzvWHPzinnOnwbdfr6g31TT6Rn0izuWbfDjdo2qrS3iZsvPbMpHTMU0fwXE1eydOnSWK95DbMAAAYASURBVLlyZbPDsBZT7OHwwJPPEm0dTBw/bsR6OJiNBpIeiYillb5zlY6NKgN6OKhtQA8HMxucE76NKs3s4WA22tWV8CUdI+mnkjYk/x92+5qkJZIekLRG0uOS3lfPPC3fmtnDwWy0q7eEfwVwT0QsBO5JPpfbB3wwIk4BzgW+JMnPCbBhaWYPB7PRrt5eOhcCy5K/vwWsAP68dISIeLLk7+2SdgIzgG7MUmpmDwez0a7ehD8rIp5N/n4OGPS+YElnAJ3AxirfXwZcBjBv3rw6Q7Oxyo+eMBueIRO+pJ8Br6nw1dWlHyIiJFXt4ylpNnAzcElEVHwVUETcANwAhW6ZQ8VmZma1GzLhR8Q51b6TtEPS7Ih4NknoFfvGSZoC/CtwdUQ8OOxozcxs2OpttF0OXJL8fQnwL+UjSOoE/hn4dkTcXuf8zMxsmOpN+H8HvF3SBuCc5DOSlkr6ejLOe4HfBD4kaXXyb0md8zUzs5T8aAUzszFksEcrtGzCl7QL2FzHJKYDz2cUTlZaMSZwXGk5rnRaMa5WjAmyiev4iJhR6YuWTfj1krSy2lmuWVoxJnBcaTmudFoxrlaMCRofl5+lY2aWE074ZmY5MZYT/g3NDqCCVowJHFdajiudVoyrFWOCBsc1ZuvwzcxsoLFcwjczsxJO+GZmOTHmEr6kcyWtl9QlqdLz+Ucqjpsk7ZT0RMmwIV8YMwJxzZV0n6S1yUtpPtYKsUk6QtIvJD2WxPXZZPgJkh5Ktuf3kkd1jChJ7ZIelXRHC8X0tKRfJneur0yGtcL+NVXS7ZL+U9I6SWc1Oy5JJ5fc5b9a0h5JH292XElsn0j29yck3ZIcBw3bv8ZUwpfUDlwPnAcsBi6WtLhJ4XyTwgtfStXywphG6wU+GRGLgTcBH0nWUbNjOwC8LSJOA5YA50p6E/D3wBcj4iTgReDSEY4L4GPAupLPrRATwFsjYklJv+1mb0OALwN3RcTrgNMorLemxhUR65P1tAR4I4WXMv1zs+OSdBzwUWBpRJwKtAMX0cj9KyLGzD/gLODuks9XAlc2MZ75wBMln9cDs5O/ZwPrW2Cd/Qvw9laKDZgIrALOpHDXYUel7TtCscyhkAzeBtwBqNkxJfN9GpheNqyp2xA4CniKpDNIq8RVFstvA/e3QlzAccAW4BgKTy6+A3hHI/evMVXC51crsGhrMqxVpHphTKNJmg+8AXiIFogtqTpZTeEx2z+l8KKc7ojoTUZpxvb8EvApoPgOh2ktEBNAAD+R9Ejy4iBo/jY8AdgFfCOpAvu6pEktEFepi4Bbkr+bGldEbAP+F/AM8CzwEvAIDdy/xlrCHzWicPpuWp9YSZOBHwAfj4g9pd81K7aI6IvCZfcc4AzgdSMdQylJvwvsjIhHmhlHFW+JiNMpVF9+RNJvln7ZpG3YAZwO/GNEvAHYS1k1STP3+6Qu/ALg++XfNSOupM3gQgonymOBSRxeDZypsZbwtwFzSz7PSYa1ih3Ji2KKbwCr+MKYRpM0jkKy/05E/LCVYgOIiG7gPgqXs1MlFV/UM9Lb883ABZKeBm6lUK3z5SbHBBwqHRIROynUR59B87fhVmBrRDyUfL6dwgmg2XEVnQesiogdyedmx3UO8FRE7IqIg8APKexzDdu/xlrCfxhYmLRyd1K4fFve5JhKDfnCmEaTJOBGYF1EfKFVYpM0Q9LU5O8JFNoV1lFI/O9pRlwRcWVEzImI+RT2pXsj4v3NjAlA0iRJRxb/plAv/QRN3oYR8RywRdLJyaCzgbXNjqvExfyqOgeaH9czwJskTUyOy+L6atz+1azGkwY2hJwPPEmh/vfqJsZxC4V6uYMUSj6XUqj/vQfYAPwMOKYJcb2FwqXr48Dq5N/5zY4NeD3waBLXE8A1yfATgV8AXRQuxcc3aXsuA+5ohZiS+T+W/FtT3M+bvQ2TGJYAK5Pt+CPg6BaJaxKwGziqZFgrxPVZ4D+Tff5mYHwj9y8/WsHMLCfGWpWOmZlV4YRvZpYTTvhmZjnhhG9mlhNO+GZmOeGEb2aWE074ZmY58f8BfSICrrj0jr0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sm.graphics.tsa.plot_pacf(a_helper.df.rtn, lags=80)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "cheap-think",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ARMA 阶次判断\n",
    "fig = plt.figure(figsize=(10, 8))\n",
    "ax1 = fig.add_subplot(211)\n",
    "fig = sm.graphics.tsa.plot_acf(a_helper.df.rtn, lags=30, ax=ax1)\n",
    "ax2 = fig.add_subplot(212)\n",
    "fig = sm.graphics.tsa.plot_pacf(a_helper.df.rtn, lags=30, ax=ax2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ideal-thanks",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:568: ConvergenceWarning: Maximum Likelihood optimization failed to converge. Check mle_retvals\n",
      "  ConvergenceWarning)\n",
      "/root/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/base/model.py:548: HessianInversionWarning: Inverting hessian failed, no bse or cov_params available\n",
      "  'available', HessianInversionWarning)\n"
     ]
    }
   ],
   "source": [
    "sic = sm.tsa.arma_order_select_ic(a_helper.df.rtn.values, max_ar=10, max_ma=10, ic='aic')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "under-mileage",
   "metadata": {},
   "outputs": [],
   "source": [
    "sic['aic_min_order']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "insured-river",
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "The computed initial AR coefficients are not stationary\nYou should induce stationarity, choose a different model order, or you can\npass your own start_params.",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-10-e7726b7a197a>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0morder\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0marma_model\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0msm\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtsa\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mARMA\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      9\u001b[0m \u001b[0mpredicts\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marma_model\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpredict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrtn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mt_len\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrtn\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdynamic\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/tsa/arima_model.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, start_params, trend, method, transparams, solver, maxiter, full_output, disp, callback, start_ar_lags, **kwargs)\u001b[0m\n\u001b[1;32m   1015\u001b[0m         \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# estimate starting parameters\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1016\u001b[0m             start_params = self._fit_start_params((k_ar, k_ma, k), method,\n\u001b[0;32m-> 1017\u001b[0;31m                                                   start_ar_lags)\n\u001b[0m\u001b[1;32m   1018\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1019\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mtransparams\u001b[0m\u001b[0;34m:\u001b[0m  \u001b[0;31m# transform initial parameters to ensure invertibility\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/tsa/arima_model.py\u001b[0m in \u001b[0;36m_fit_start_params\u001b[0;34m(self, order, method, start_ar_lags)\u001b[0m\n\u001b[1;32m    606\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0;34m-\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mloglike_css\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mparams\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    607\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 608\u001b[0;31m             \u001b[0mstart_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_fit_start_params_hr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstart_ar_lags\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    609\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtransparams\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    610\u001b[0m                 \u001b[0mstart_params\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_invtransparams\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstart_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/.pyenv/versions/3.7.0/lib/python3.7/site-packages/statsmodels/tsa/arima_model.py\u001b[0m in \u001b[0;36m_fit_start_params_hr\u001b[0;34m(self, order, start_ar_lags)\u001b[0m\n\u001b[1;32m    584\u001b[0m         if p and not np.all(np.abs(np.roots(np.r_[1, -start_params[k:k + p]]\n\u001b[1;32m    585\u001b[0m                                             )) < 1):\n\u001b[0;32m--> 586\u001b[0;31m             raise ValueError(\"The computed initial AR coefficients are not \"\n\u001b[0m\u001b[1;32m    587\u001b[0m                              \u001b[0;34m\"stationary\\nYou should induce stationarity, \"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    588\u001b[0m                              \u001b[0;34m\"choose a different model order, or you can\\n\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: The computed initial AR coefficients are not stationary\nYou should induce stationarity, choose a different model order, or you can\npass your own start_params."
     ]
    }
   ],
   "source": [
    "t_len = 20\n",
    "\n",
    "train = rtn[:-t_len]\n",
    "test = rtn[-t_len:]\n",
    "\n",
    "order = (2,3)\n",
    "arma_model = sm.tsa.ARMA(train, order).fit()\n",
    "\n",
    "predicts = arma_model.predict(len(rtn) - t_len, len(rtn) - 1, dynamic=True)\n",
    "\n",
    "comp = pd.DataFrame()\n",
    "comp['original'] = test\n",
    "comp['predict'] = predicts\n",
    "comp.plot(figsize=(10,5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "general-fitness",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 对数收益率:\n",
    "\n",
    "ts_log = np.log(ts)\n",
    "ts_diff = ts_log.diff(1)\n",
    "ts_diff.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "instant-message",
   "metadata": {},
   "outputs": [],
   "source": [
    "# http://www.360doc.com/content/18/0408/12/1489589_743766179.shtml\n",
    "\n",
    "def plot_ts(ts,w, title='plot_ts'):\n",
    "    roll_mean = ts.rolling(window = w).mean()\n",
    "    roll_std = ts.rolling(window = w).std()\n",
    "    pd_ewma = pd.DataFrame.ewm(ts, span=w).mean()\n",
    "    \n",
    "    plt.clf()\n",
    "    plt.figure()\n",
    "    plt.grid()\n",
    "    plt.plot(ts, color='blue', label='Opriginal')\n",
    "    plt.plot(roll_mean, color='red', label='Rolling Mean')\n",
    "    plt.plot(roll_std, color='Black', label='Std')\n",
    "    plt.plot(pd_ewma, color='yellow', label='EWMA')\n",
    "    plt.legend(loc='best')\n",
    "    plt.title('Rolling Mean')\n",
    "    plt.show()\n",
    "    plt.savefig('./PDF/'+title+'.pdf', format='pdf')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "arabic-daughter",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_ts(ts,20,'test')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
